ABSTRACT:In response to natural disasters, efficient planning for optimum allocation of the medical assistance to wounded as fast as possible and wayfinding of first responders immediately to minimize the risk of natural disasters are of prime importance. This paper aims to propose a multi-agent based modeling for optimum allocation of space to emergency centers according to the population, street network and number of ambulances in emergency centers by constraint network Voronoi diagrams, wayfinding of ambulances from emergency centers to the wounded locations and return based on the minimum ambulances travel time and path length implemented by NSGA and the use of smart city facilities to accelerate the rescue operation. Simulated annealing algorithm has been used for minimizing the difference between demands and supplies of the constrained network Voronoi diagrams. In the proposed multi-agent system, after delivering the location of the wounded and their symptoms, the constraint network Voronoi diagram for each emergency center is determined. This process was performed simultaneously for the multi-injuries in different Voronoi diagrams. In the proposed multiagent system, the priority of the injuries for receiving medical assistance and facilities of the smart city for reporting the blocked streets was considered. Tehran Municipality District 5 was considered as the study area and during 3 minutes intervals, the volunteers reported the blocked street. The difference between the supply and the demand divided to the supply in each Voronoi diagram decreased to 0.1601. In the proposed multi-agent system, the response time of the ambulances is decreased about 36.7%.

<p><strong>Abstract.</strong> After the incidence of a disaster, a high demand for first-aid and a huge number of injured will emerge at the affected areas. In this paper, the optimum allocation of the medical assistance to the injured according to a multi-criteria decision making is performed by Multiplicatively Weighted Network Voronoi Diagram (MWNVD). For consideration of the allocation of the injured in the affected area to the appropriate hospitals using the MWNVD and decreasing the gap between the estimated and expected population in the MWNVDs, Particle Swarm Optimization (PSO) is applied to the MWNVDs.<br> This paper proposes a multi agent-based modeling for incorporating the allocation of the medical supplies to the injured according to the generated Voronoi Diagrams of the PSO-MWNVD, wayfinding of emergency vehicles based on the minimum travel distance and time as well as using smart city facilities to expedite the rescue operation. In the proposed model, considering the priority of the injured for receiving the medical assistance, information transfer about the condition of the injured to the hospitals prior to ambulance arrival for providing appropriate treatment, updating of emergency vehicles route based on the blocked streets and etc. are optimized.<br> The partial difference between the estimated and expected population for receiving the medical assistance in MWNVDs is computed as 37&amp;thinsp;%, while the PSO-MWNVD decreased the mentioned difference to 6&amp;thinsp;%. The relief operation time in the proposed model compared to another multi-agent rescue operation model, which uses MWNVD and does not have some facilities of the proposed model, is improved.</p>

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ABSTRACT:This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

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